{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'QuanSongCi.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data into a list of strings.\n",
    "def read_data(filename):\n",
    "  with open(filename, 'r', encoding='utf-8') as f:\n",
    "    data = list(f.read())\n",
    "  return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1903073\n"
     ]
    }
   ],
   "source": [
    "vocabulary = read_data(filename)\n",
    "print('Data size', len(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary_size = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(words, n_words):\n",
    "  \"\"\"Process raw inputs into a dataset.\"\"\"\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(n_words - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    index = dictionary.get(word, 0)\n",
    "    if index == 0:  # dictionary['UNK']\n",
    "      unk_count += 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "  return data, count, dictionary, reversed_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,\n",
    "                                                            vocabulary_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dictionary.json','w',encoding='utf-8') as inf:\n",
    "    json.dump(dictionary,inf)\n",
    "    inf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('reverse_dictionary.json','w',encoding='utf-8') as inf:\n",
    "    json.dump(reverse_dictionary,inf)\n",
    "    inf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 1196], ('。', 149620), ('\\n', 117070), ('，', 108451), ('、', 19612)]\n",
      "Sample data [1503, 1828, 2, 2, 40, 613, 47, 9, 111, 117] ['潘', '阆', '\\n', '\\n', '酒', '泉', '子', '（', '十', '之']\n"
     ]
    }
   ],
   "source": [
    "del vocabulary  # Hint to reduce memory.\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_index = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1  # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  if data_index + span > len(data):\n",
    "    data_index = 0\n",
    "  buffer.extend(data[data_index:data_index + span])\n",
    "  data_index += span\n",
    "  for i in range(batch_size // num_skips):\n",
    "    context_words = [w for w in range(span) if w != skip_window]\n",
    "    words_to_use = random.sample(context_words, num_skips)\n",
    "    for j, context_word in enumerate(words_to_use):\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[context_word]\n",
    "    if data_index == len(data):\n",
    "      #buffer[:] = data[:span]\n",
    "      buffer.extend(data[0:span])\n",
    "      \"\"\"\n",
    "      for word in data[:span]:\n",
    "        buffer.append(word)\n",
    "      \"\"\"\n",
    "      data_index = span\n",
    "    else:\n",
    "      buffer.append(data[data_index])\n",
    "      data_index += 1\n",
    "  # Backtrack a little bit to avoid skipping words in the end of a batch\n",
    "  data_index = (data_index + len(data) - span) % len(data)\n",
    "  return batch, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1828 阆 -> 1503 潘\n",
      "1828 阆 -> 2 \n",
      "\n",
      "2 \n",
      " -> 1828 阆\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "2 \n",
      " -> 40 酒\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "40 酒 -> 613 泉\n",
      "40 酒 -> 2 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n",
    "for i in range(8):\n",
    "  print(batch[i], reverse_dictionary[batch[i]],\n",
    "        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64      # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "\n",
    "learning_rate = 1.0\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "graph = tf.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-17-5bfc1a4b5baa>:38: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    }
   ],
   "source": [
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "  # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "  with tf.device('/cpu:0'):\n",
    "    # Look up embeddings for inputs.\n",
    "    embeddings = tf.Variable(\n",
    "        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "    embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "\n",
    "    # Construct the variables for the NCE loss\n",
    "    nce_weights = tf.Variable(\n",
    "        tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                            stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "  # Compute the average NCE loss for the batch.\n",
    "  # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "  # time we evaluate the loss.\n",
    "  # Explanation of the meaning of NCE loss:\n",
    "  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/\n",
    "  loss = tf.reduce_mean(\n",
    "      tf.nn.nce_loss(weights=nce_weights,\n",
    "                     biases=nce_biases,\n",
    "                     labels=train_labels,\n",
    "                     inputs=embed,\n",
    "                     num_sampled=num_sampled,\n",
    "                     num_classes=vocabulary_size))\n",
    "\n",
    "  # Construct the SGD optimizer using a learning rate of 1.0.  \n",
    "  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n",
    "\n",
    "  # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "      normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(\n",
    "      valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "  # Add variable initializer.\n",
    "  init = tf.global_variables_initializer()\n",
    "\n",
    "  saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_steps = 500001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/fei/AI/csdn课程/第11周/作业/quiz-w10-code/ckpt/model-500000\n",
      "Average loss at step  0 :  4.239289283752441\n",
      "Nearest to 子: 牧, 跌, 乙, 乐, 姆, 潞, 荛, 闤,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 却, 总, 荏, 渐,\n",
      "Nearest to 醉: 笑, 睡, 醒, 酣, 困, 醺, 恣, 拚,\n",
      "Nearest to 云: 烟, 霞, 霭, 雾, 颅, 旆, 缥, 山,\n",
      "Nearest to 何: 多, 否, 邂, 昨, 还, 焉, 无, 那,\n",
      "Nearest to 未: 不, 乍, 初, 已, 易, 也, 难, 顿,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 对, 向, 想, 摘,\n",
      "Nearest to 天: 逍, 快, 帝, 云, 霓, 罨, 朗, 蠲,\n",
      "Nearest to 如: 似, 奈, 藁, 而, 漠, 鳏, 旱, 躇,\n",
      "Nearest to 水: 江, 山, 波, 颅, 溪, 睥, 澹, 泠,\n",
      "Nearest to 、: ，, 。, 怕, （, 尽, 酤, 恁, 苞,\n",
      "Nearest to 长: 永, 平, 当, 殇, 逢, 滚, 遥, 亶,\n",
      "Nearest to 歌: 唱, 舞, 箫, 遏, 曲, 乐, 阕, 硬,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 熄, 鄞, 葛,\n",
      "Nearest to 酒: 屠, 盏, 杯, 蘋, 酿, 醺, 釂, 篘,\n",
      "Nearest to 此: 今, 兹, 每, 予, 良, 幸, 毋, 何,\n",
      "Average loss at step  2000 :  3.811271740913391\n",
      "Average loss at step  4000 :  3.859837305665016\n",
      "Average loss at step  6000 :  3.88313531768322\n",
      "Average loss at step  8000 :  3.827462136387825\n",
      "Average loss at step  10000 :  3.8308524883687496\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 儿, 荛, 乙, 姆,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 总, 败, 釂,\n",
      "Nearest to 醉: 笑, 酣, 醒, 睡, 恣, 饮, 困, 须,\n",
      "Nearest to 云: 烟, 雾, 旆, 霞, 天, 杳, 缥, 霭,\n",
      "Nearest to 何: 岂, 否, 多, 邂, 无, 昨, 那, 焉,\n",
      "Nearest to 未: 不, 乍, 初, 已, 欲, 先, 也, 易,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 把, 摘, 与, 向,\n",
      "Nearest to 天: 云, 快, 空, 逍, 怡, 罨, 己, 朗,\n",
      "Nearest to 如: 似, 鳏, 漠, 藁, 旱, 奈, 间, 胥,\n",
      "Nearest to 水: 江, 山, 溪, 海, 颅, 碧, 睥, 蹲,\n",
      "Nearest to 、: ，, 。, （, ；, 閤, 酤, 似, 待,\n",
      "Nearest to 长: 永, 逢, 当, 平, 常, 殇, 自, 矰,\n",
      "Nearest to 歌: 唱, 箫, 乐, 曲, 遏, 舞, 酺, 阕,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 熄, 阡, 头, 葛,\n",
      "Nearest to 酒: 屠, 醪, 酿, 釂, 盏, 杯, 醑, 柑,\n",
      "Nearest to 此: 每, 兹, 今, 毋, 良, 予, 幸, 朅,\n",
      "Average loss at step  12000 :  3.8720256943702696\n",
      "Average loss at step  14000 :  3.8640610896348955\n",
      "Average loss at step  16000 :  3.8941541635990142\n",
      "Average loss at step  18000 :  3.926900759935379\n",
      "Average loss at step  20000 :  3.90381880235672\n",
      "Nearest to 子: 牧, 跌, 乙, 潞, 乐, 荛, 姆, 闤,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 荏, 败, 渐,\n",
      "Nearest to 醉: 醒, 睡, 笑, 酣, 困, 羞, 醺, 饮,\n",
      "Nearest to 云: 烟, 旆, 颅, 雾, 缥, 髭, 杳, 山,\n",
      "Nearest to 何: 焉, 多, 邂, 底, 岂, 那, 昨, 否,\n",
      "Nearest to 未: 不, 乍, 已, 易, 初, 欲, 裛, 难,\n",
      "Nearest to 看: 觑, 趁, 见, 对, 听, 向, 与, 把,\n",
      "Nearest to 天: 逍, 快, 帝, 空, 庸, 蠲, 霓, 荏,\n",
      "Nearest to 如: 似, 漠, 藁, 奈, 鳏, 在, 践, 陀,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 波, 睥, 涎, 蹲,\n",
      "Nearest to 、: ，, 。, 怕, （, 苑, 颤, 说, 共,\n",
      "Nearest to 长: 殇, 永, 平, 当, 逢, 健, 遥, 滚,\n",
      "Nearest to 歌: 唱, 曲, 舞, 乐, 遏, 侑, 郢, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 头, 熄, 葛,\n",
      "Nearest to 酒: 屠, 醪, 酿, 盏, 醑, 杯, 斟, 釂,\n",
      "Nearest to 此: 今, 兹, 每, 幸, 予, 良, 毋, 朅,\n",
      "Average loss at step  22000 :  3.9410456166267394\n",
      "Average loss at step  24000 :  3.91437060880661\n",
      "Average loss at step  26000 :  3.92005450463295\n",
      "Average loss at step  28000 :  3.947467041373253\n",
      "Average loss at step  30000 :  3.917200621724129\n",
      "Nearest to 子: 牧, 跌, 潞, 乙, 乐, 儿, 鸪, 籍,\n",
      "Nearest to 尽: 了, 遍, 都, 总, 竟, 却, 荏, 渐,\n",
      "Nearest to 醉: 酣, 笑, 睡, 醒, 醺, 困, 饮, 须,\n",
      "Nearest to 云: 烟, 雾, 旆, 霞, 缥, 天, 颅, 髭,\n",
      "Nearest to 何: 岂, 焉, 邂, 多, 昨, 无, 那, 否,\n",
      "Nearest to 未: 不, 难, 乍, 易, 已, 初, 也, 顿,\n",
      "Nearest to 看: 见, 趁, 觑, 向, 听, 对, 摘, 把,\n",
      "Nearest to 天: 逍, 云, 快, 自, 山, 怡, 霓, 庸,\n",
      "Nearest to 如: 似, 漠, 藁, 奈, 鳏, 旱, 陀, 而,\n",
      "Nearest to 水: 江, 山, 睥, 泉, 派, 泠, 波, 蹲,\n",
      "Nearest to 、: ，, 。, （, 怕, 眨, ；, 恁, 楯,\n",
      "Nearest to 长: 永, 平, 当, 阊, 逢, 殇, 常, 正,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 遏, 阕, 曲, 郢,\n",
      "Nearest to 南: 北, 西, 阡, 东, 熄, 葛, 鄞, 头,\n",
      "Nearest to 酒: 屠, 醺, 酿, 蘋, 盏, 醪, 杯, 篘,\n",
      "Nearest to 此: 今, 兹, 每, 良, 幸, 旧, 毋, 年,\n",
      "Average loss at step  32000 :  3.83134565615654\n",
      "Average loss at step  34000 :  3.855050886750221\n",
      "Average loss at step  36000 :  3.8917055323123932\n",
      "Average loss at step  38000 :  3.831842257618904\n",
      "Average loss at step  40000 :  3.829099857389927\n",
      "Nearest to 子: 牧, 跌, 潞, 鸪, 乙, 煅, 荛, 缵,\n",
      "Nearest to 尽: 了, 遍, 都, 渐, 竟, 总, 共, 半,\n",
      "Nearest to 醉: 笑, 睡, 饮, 酣, 醒, 恣, 须, 困,\n",
      "Nearest to 云: 烟, 雾, 旆, 缥, 霞, 杳, 髭, 蓂,\n",
      "Nearest to 何: 岂, 多, 邂, 蒻, 无, 否, 昨, 不,\n",
      "Nearest to 未: 不, 乍, 已, 初, 也, 欲, 先, 易,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 把, 拂, 摘, 向,\n",
      "Nearest to 天: 空, 快, 逍, 霄, 云, 怡, 罨, 朗,\n",
      "Nearest to 如: 似, 鳏, 藁, 而, 旱, 陀, 奈, 漠,\n",
      "Nearest to 水: 江, 山, 溪, 纣, 颅, 派, 睥, 蹲,\n",
      "Nearest to 、: ，, 。, ；, 閤, （, 眨, 似, 惘,\n",
      "Nearest to 长: 永, 逢, 常, 平, 殇, 当, 修, 跎,\n",
      "Nearest to 歌: 唱, 乐, 遏, 箫, 舞, 曲, 侑, 咽,\n",
      "Nearest to 南: 北, 西, 东, 熄, 鄞, 阡, 楚, 头,\n",
      "Nearest to 酒: 屠, 酿, 醪, 盏, 釂, 醑, 杯, 斟,\n",
      "Nearest to 此: 每, 兹, 今, 予, 毋, 幸, 良, 一,\n",
      "Average loss at step  42000 :  3.8641249545812606\n",
      "Average loss at step  44000 :  3.8782363114356992\n",
      "Average loss at step  46000 :  3.9126245675086975\n",
      "Average loss at step  48000 :  3.922164429306984\n",
      "Average loss at step  50000 :  3.9089736213684083\n",
      "Nearest to 子: 牧, 跌, 乙, 乐, 荛, 潞, 姆, 缵,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 奈, 荏, 败,\n",
      "Nearest to 醉: 睡, 笑, 醒, 酣, 困, 醺, 饮, 须,\n",
      "Nearest to 云: 烟, 颅, 旆, 缥, 。, 杳, 髭, 霞,\n",
      "Nearest to 何: 多, 焉, 邂, 昨, 岂, 甚, 还, 那,\n",
      "Nearest to 未: 不, 乍, 已, 易, 欲, 难, 也, 初,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 对, 向, 与, 把,\n",
      "Nearest to 天: 逍, 快, 帝, 罨, 春, 空, 淝, 山,\n",
      "Nearest to 如: 似, 漠, 藁, 奈, 鳏, 在, 。, 于,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 蹲, 波, 涎, 纣,\n",
      "Nearest to 、: ，, 。, 怕, （, 纻, ；, 作, 苑,\n",
      "Nearest to 长: 永, 殇, 平, 当, 遥, 逢, 牢, 常,\n",
      "Nearest to 歌: 唱, 舞, 曲, 遏, 箫, 乐, 阕, 郢,\n",
      "Nearest to 南: 北, 西, 阡, 东, 熄, 头, 鄞, 葛,\n",
      "Nearest to 酒: 屠, 酿, 醪, 盏, 醑, 杯, 斟, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 幸, 良, 予, 毋, 朅,\n",
      "Average loss at step  52000 :  3.9354915244579316\n",
      "Average loss at step  54000 :  3.9096644978523254\n",
      "Average loss at step  56000 :  3.913724987387657\n",
      "Average loss at step  58000 :  3.9318016543388365\n",
      "Average loss at step  60000 :  3.8898296933174135\n",
      "Nearest to 子: 牧, 跌, 潞, 乙, 乐, 儿, 籍, 鸪,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 共, 渐, 却,\n",
      "Nearest to 醉: 笑, 酣, 睡, 醒, 饮, 醺, 困, 恣,\n",
      "Nearest to 云: 旆, 雾, 烟, 天, 缥, 霞, 颅, 鸾,\n",
      "Nearest to 何: 岂, 多, 邂, 无, 焉, 昨, 否, 那,\n",
      "Nearest to 未: 不, 难, 乍, 已, 易, 顿, 欲, 也,\n",
      "Nearest to 看: 见, 觑, 趁, 向, 听, 摘, 把, 与,\n",
      "Nearest to 天: 云, 逍, 山, 快, 庸, 罨, 忡, 自,\n",
      "Nearest to 如: 似, 奈, 漠, 藁, 旱, 鳏, 而, 陀,\n",
      "Nearest to 水: 江, 山, 波, 派, 碧, 溪, 睥, 渺,\n",
      "Nearest to 、: ，, 。, （, 离, 怕, 觑, 逶, 楯,\n",
      "Nearest to 长: 永, 当, 平, 逢, 常, 殇, 阊, 亶,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 侑, 遏, 阕, 郢,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 葛, 鄞, 头,\n",
      "Nearest to 酒: 屠, 醺, 杯, 盏, 蘋, 酿, 釂, 醑,\n",
      "Nearest to 此: 今, 兹, 每, 幸, 良, 我, 毋, 旧,\n",
      "Average loss at step  62000 :  3.8501126133203507\n",
      "Average loss at step  64000 :  3.8607083430290223\n",
      "Average loss at step  66000 :  3.8682785514593125\n",
      "Average loss at step  68000 :  3.8299406161308287\n",
      "Average loss at step  70000 :  3.8379538592100144\n",
      "Nearest to 子: 牧, 跌, 潞, 鸪, 乙, 荛, 缵, 煅,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 总, 败, 半,\n",
      "Nearest to 醉: 笑, 酣, 醒, 睡, 饮, 恣, 困, 釂,\n",
      "Nearest to 云: 烟, 旆, 雾, 缥, 颅, 杳, 霞, 缔,\n",
      "Nearest to 何: 岂, 多, 邂, 蒻, 焉, 否, 无, 昨,\n",
      "Nearest to 未: 不, 已, 乍, 初, 欲, 先, 难, 易,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 听, 把, 与, 拂,\n",
      "Nearest to 天: 快, 空, 逍, 罨, 怡, 莹, 帝, 蠲,\n",
      "Nearest to 如: 似, 鳏, 藁, 漠, 奈, 旱, 胥, 依,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 碧, 睥, 纣, 派,\n",
      "Nearest to 、: ，, 。, （, ；, 眨, 閤, 伴, 崆,\n",
      "Nearest to 长: 永, 常, 平, 逢, 殇, 修, 跎, 当,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 舞, 曲, 酺, 侑,\n",
      "Nearest to 南: 北, 西, 东, 熄, 鄞, 头, 阡, 楚,\n",
      "Nearest to 酒: 屠, 釂, 盏, 醪, 酿, 醑, 杯, 斟,\n",
      "Nearest to 此: 兹, 每, 今, 一, 幸, 我, 予, 毋,\n",
      "Average loss at step  72000 :  3.864754096508026\n",
      "Average loss at step  74000 :  3.873850431919098\n",
      "Average loss at step  76000 :  3.9311534914970396\n",
      "Average loss at step  78000 :  3.9090644994974135\n",
      "Average loss at step  80000 :  3.9196330350637436\n",
      "Nearest to 子: 牧, 跌, 乙, 乐, 荛, 潞, 姆, 鸪,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 共, 败, 荏,\n",
      "Nearest to 醉: 睡, 笑, 醒, 酣, 困, 醺, 羞, 须,\n",
      "Nearest to 云: 烟, 旆, 颅, 缥, 雾, 霞, 髭, 杳,\n",
      "Nearest to 何: 多, 焉, 邂, 岂, 甚, 那, 还, 昨,\n",
      "Nearest to 未: 不, 乍, 已, 欲, 初, 易, 也, 难,\n",
      "Nearest to 看: 觑, 见, 趁, 对, 听, 向, 与, 摘,\n",
      "Nearest to 天: 快, 逍, 空, 罨, 帝, 春, 蠲, 霓,\n",
      "Nearest to 如: 似, 藁, 漠, 奈, 鳏, 在, 旱, 佣,\n",
      "Nearest to 水: 江, 溪, 山, 颅, 蹲, 渚, 波, 涎,\n",
      "Nearest to 、: ，, 。, ；, 怕, （, 作, 忆, 想,\n",
      "Nearest to 长: 平, 当, 永, 殇, 逢, 牢, 萧, 滚,\n",
      "Nearest to 歌: 唱, 曲, 舞, 箫, 遏, 乐, 郢, 侑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 葛,\n",
      "Nearest to 酒: 屠, 盏, 醪, 酿, 杯, 釂, 醑, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 幸, 予, 良, 朅, 毋,\n",
      "Average loss at step  82000 :  3.93177588891983\n",
      "Average loss at step  84000 :  3.90358061003685\n",
      "Average loss at step  86000 :  3.9257741852998733\n",
      "Average loss at step  88000 :  3.9218505129814147\n",
      "Average loss at step  90000 :  3.8706736575365066\n",
      "Nearest to 子: 牧, 跌, 潞, 乙, 乐, 鸪, 籍, 荛,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 却, 奈, 渐,\n",
      "Nearest to 醉: 笑, 酣, 醒, 睡, 醺, 饮, 困, 宴,\n",
      "Nearest to 云: 天, 旆, 雾, 缥, 霞, 烟, 鸾, 颅,\n",
      "Nearest to 何: 岂, 多, 无, 焉, 邂, 否, 那, 昨,\n",
      "Nearest to 未: 不, 乍, 难, 欲, 易, 已, 初, 也,\n",
      "Nearest to 看: 见, 觑, 趁, 把, 对, 听, 向, 呼,\n",
      "Nearest to 天: 云, 逍, 快, 山, 罨, 空, 霓, 怡,\n",
      "Nearest to 如: 似, 漠, 藁, 鳏, 旱, 奈, 陀, 为,\n",
      "Nearest to 水: 山, 江, 波, 派, 睥, 溪, 渚, 泠,\n",
      "Nearest to 、: ，, 。, （, 恁, ；, 添, 楯, 轣,\n",
      "Nearest to 长: 永, 当, 平, 逢, 常, 正, 跎, 殇,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 遏, 郢, 侑, 曲,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 葛, 头, 鄞,\n",
      "Nearest to 酒: 屠, 盏, 酿, 杯, 釂, 醑, 醺, 篘,\n",
      "Nearest to 此: 兹, 今, 每, 幸, 年, 良, 何, 旧,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  92000 :  3.847808943748474\n",
      "Average loss at step  94000 :  3.877649039745331\n",
      "Average loss at step  96000 :  3.845300874590874\n",
      "Average loss at step  98000 :  3.8405739010572435\n",
      "Average loss at step  100000 :  3.8424102200865744\n",
      "Nearest to 子: 跌, 牧, 潞, 鸪, 荛, 缵, 乙, 姆,\n",
      "Nearest to 尽: 了, 遍, 都, 渐, 竟, 总, 半, 败,\n",
      "Nearest to 醉: 笑, 睡, 饮, 醒, 酣, 须, 恣, 拚,\n",
      "Nearest to 云: 雾, 烟, 缥, 旆, 霞, 颅, 杳, 缔,\n",
      "Nearest to 何: 无, 岂, 邂, 多, 否, 昨, 蒻, 不,\n",
      "Nearest to 未: 不, 已, 初, 乍, 欲, 先, 难, 才,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 摘, 与, 把, 拂,\n",
      "Nearest to 天: 空, 快, 罨, 逍, 蠲, 怡, 帝, 莹,\n",
      "Nearest to 如: 似, 鳏, 旱, 藁, 漠, 依, 胥, 而,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 纣, 睥, 碧, 派,\n",
      "Nearest to 、: ，, 。, （, ；, 是, 惘, 奈, 怕,\n",
      "Nearest to 长: 永, 逢, 常, 殇, 平, 当, 修, 跎,\n",
      "Nearest to 歌: 唱, 遏, 乐, 箫, 舞, 咽, 曲, 侑,\n",
      "Nearest to 南: 北, 西, 东, 熄, 鄞, 阡, 头, 楚,\n",
      "Nearest to 酒: 屠, 盏, 酿, 釂, 醑, 醪, 杯, 斟,\n",
      "Nearest to 此: 兹, 每, 今, 一, 我, 良, 幸, 敧,\n",
      "Average loss at step  102000 :  3.8613646107912065\n",
      "Average loss at step  104000 :  3.884446521639824\n",
      "Average loss at step  106000 :  3.925555533885956\n",
      "Average loss at step  108000 :  3.900362438440323\n",
      "Average loss at step  110000 :  3.9356039980649946\n",
      "Nearest to 子: 牧, 跌, 乙, 乐, 姆, 潞, 鸪, 荛,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 奈, 荏, 却,\n",
      "Nearest to 醉: 醒, 睡, 笑, 酣, 困, 醺, 须, 羞,\n",
      "Nearest to 云: 烟, 颅, 旆, 雾, 缥, 髭, 纣, 杳,\n",
      "Nearest to 何: 邂, 焉, 岂, 多, 还, 那, 昨, 底,\n",
      "Nearest to 未: 不, 乍, 已, 易, 难, 初, 欲, 裛,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 对, 向, 照, 拂,\n",
      "Nearest to 天: 快, 逍, 帝, 自, 春, 蠲, 庸, 己,\n",
      "Nearest to 如: 似, 藁, 奈, 漠, 鳏, 旱, 鷁, 胥,\n",
      "Nearest to 水: 江, 山, 颅, 溪, 蹲, 涎, 睥, 纣,\n",
      "Nearest to 、: ，, 。, ；, （, 怕, 恐, 酤, 春,\n",
      "Nearest to 长: 当, 殇, 平, 永, 逢, 滚, 牢, 健,\n",
      "Nearest to 歌: 唱, 舞, 遏, 曲, 箫, 乐, 侑, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 楚, 葛,\n",
      "Nearest to 酒: 屠, 盏, 醪, 酿, 杯, 釂, 醑, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 予, 幸, 朅, 一, 毋,\n",
      "Average loss at step  112000 :  3.9172261838912963\n",
      "Average loss at step  114000 :  3.9085080811977386\n",
      "Average loss at step  116000 :  3.9158326733112334\n",
      "Average loss at step  118000 :  3.925838256716728\n",
      "Average loss at step  120000 :  3.8404624233245848\n",
      "Nearest to 子: 跌, 牧, 潞, 乙, 乐, 鸪, 儿, 姆,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 共, 奈, 渐,\n",
      "Nearest to 醉: 笑, 酣, 睡, 醒, 醺, 饮, 困, 宴,\n",
      "Nearest to 云: 旆, 天, 雾, 烟, 缥, 霞, 颅, 杳,\n",
      "Nearest to 何: 无, 多, 焉, 邂, 岂, 昨, 否, 甚,\n",
      "Nearest to 未: 不, 乍, 难, 易, 欲, 初, 已, 也,\n",
      "Nearest to 看: 觑, 见, 趁, 向, 对, 把, 听, 摘,\n",
      "Nearest to 天: 云, 逍, 快, 忡, 怡, 山, 自, 庸,\n",
      "Nearest to 如: 似, 藁, 鳏, 漠, 奈, 旱, 陀, 而,\n",
      "Nearest to 水: 江, 山, 波, 派, 溪, 睥, 海, 蹲,\n",
      "Nearest to 、: ，, 。, 恁, 眨, （, 酤, 添, 觑,\n",
      "Nearest to 长: 永, 当, 逢, 平, 常, 正, 殇, 跎,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 侑, 郢, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 鄞, 葛, 头,\n",
      "Nearest to 酒: 屠, 盏, 酿, 釂, 醪, 杯, 醑, 蘋,\n",
      "Nearest to 此: 今, 兹, 每, 幸, 年, 良, 旧, 有,\n",
      "Average loss at step  122000 :  3.8539118374586105\n",
      "Average loss at step  124000 :  3.883240090608597\n",
      "Average loss at step  126000 :  3.8323102279901504\n",
      "Average loss at step  128000 :  3.835338060259819\n",
      "Average loss at step  130000 :  3.8511934878230094\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 姆, 荛, 缵, 乙,\n",
      "Nearest to 尽: 了, 遍, 都, 渐, 竟, 共, 总, 半,\n",
      "Nearest to 醉: 笑, 饮, 睡, 须, 醒, 酣, 宴, 困,\n",
      "Nearest to 云: 雾, 烟, 旆, 缥, 霞, 姗, 颅, 霭,\n",
      "Nearest to 何: 岂, 邂, 多, 焉, 那, 昨, 否, 无,\n",
      "Nearest to 未: 不, 初, 已, 乍, 欲, 先, 也, 易,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 与, 把, 想, 观,\n",
      "Nearest to 天: 空, 快, 罨, 逍, 月, 莹, 云, 璀,\n",
      "Nearest to 如: 似, 鳏, 藁, 旱, 依, 漠, 胥, 而,\n",
      "Nearest to 水: 江, 山, 颅, 溪, 碧, 睥, 纣, 派,\n",
      "Nearest to 、: ，, 。, （, ；, 酤, 閤, 入, 冱,\n",
      "Nearest to 长: 永, 逢, 殇, 修, 常, 平, 当, 跎,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 舞, 咽, 曲, 侑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 葛,\n",
      "Nearest to 酒: 屠, 釂, 盏, 杯, 醑, 醪, 酿, 篘,\n",
      "Nearest to 此: 兹, 每, 今, 一, 予, 敧, 良, 我,\n",
      "Average loss at step  132000 :  3.8501100583076475\n",
      "Average loss at step  134000 :  3.8836710159778596\n",
      "Average loss at step  136000 :  3.9279141327142715\n",
      "Average loss at step  138000 :  3.8931371124982834\n",
      "Average loss at step  140000 :  3.938911405920982\n",
      "Nearest to 子: 牧, 跌, 荛, 姆, 乙, 潞, 乐, 鸪,\n",
      "Nearest to 尽: 了, 遍, 都, 总, 竟, 奈, 荏, 败,\n",
      "Nearest to 醉: 醒, 睡, 酣, 笑, 醺, 困, 饮, 宴,\n",
      "Nearest to 云: 烟, 颅, 缥, 旆, 髭, 山, 霞, 汛,\n",
      "Nearest to 何: 焉, 邂, 多, 岂, 那, 还, 今, 昨,\n",
      "Nearest to 未: 不, 乍, 已, 易, 欲, 难, 顿, 也,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 见, 向, 与, 任,\n",
      "Nearest to 天: 逍, 快, 帝, 蠲, 空, 霓, 莹, 云,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 鳏, 旱, 佣, 鷁,\n",
      "Nearest to 水: 江, 山, 颅, 溪, 涎, 蹲, 波, 派,\n",
      "Nearest to 、: ，, 。, ；, 怕, （, 恐, 得, 苞,\n",
      "Nearest to 长: 平, 逢, 当, 永, 殇, 滚, 牢, 修,\n",
      "Nearest to 歌: 唱, 舞, 乐, 曲, 遏, 箫, 侑, 酺,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 楚, 头,\n",
      "Nearest to 酒: 屠, 醪, 盏, 酿, 醑, 杯, 蘋, 釂,\n",
      "Nearest to 此: 兹, 今, 每, 予, 幸, 朅, 良, 我,\n",
      "Average loss at step  142000 :  3.9235538518428803\n",
      "Average loss at step  144000 :  3.904041269659996\n",
      "Average loss at step  146000 :  3.919531107187271\n",
      "Average loss at step  148000 :  3.917898445367813\n",
      "Average loss at step  150000 :  3.8253496439456938\n",
      "Nearest to 子: 跌, 牧, 潞, 儿, 鸪, 乐, 乙, 荛,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 共, 渐, 奈,\n",
      "Nearest to 醉: 笑, 酣, 睡, 醺, 醒, 饮, 困, 酒,\n",
      "Nearest to 云: 旆, 雾, 缥, 烟, 天, 霞, 颅, 鸾,\n",
      "Nearest to 何: 无, 邂, 多, 岂, 焉, 那, 否, 昨,\n",
      "Nearest to 未: 不, 难, 乍, 已, 易, 欲, 顿, 也,\n",
      "Nearest to 看: 觑, 见, 趁, 向, 听, 把, 对, 摘,\n",
      "Nearest to 天: 逍, 云, 快, 罨, 空, 忡, 怡, 自,\n",
      "Nearest to 如: 似, 藁, 奈, 鳏, 而, 成, 漠, 旱,\n",
      "Nearest to 水: 江, 山, 波, 派, 泉, 溪, 睥, 泠,\n",
      "Nearest to 、: ，, 。, 添, 眨, 恁, （, 愁, 觑,\n",
      "Nearest to 长: 永, 平, 逢, 当, 滚, 正, 遥, 常,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 郢, 曲, 侑,\n",
      "Nearest to 南: 西, 北, 东, 阡, 熄, 葛, 鄞, 湖,\n",
      "Nearest to 酒: 盏, 屠, 酿, 醺, 杯, 醑, 醪, 篘,\n",
      "Nearest to 此: 今, 兹, 每, 良, 旧, 幸, 何, 年,\n",
      "Average loss at step  152000 :  3.8586636370420457\n",
      "Average loss at step  154000 :  3.8840362901687624\n",
      "Average loss at step  156000 :  3.8314248073101043\n",
      "Average loss at step  158000 :  3.834908269047737\n",
      "Average loss at step  160000 :  3.85522548943758\n",
      "Nearest to 子: 牧, 跌, 潞, 鸪, 荛, 姆, 缵, 乙,\n",
      "Nearest to 尽: 了, 遍, 都, 渐, 竟, 败, 半, 共,\n",
      "Nearest to 醉: 笑, 饮, 须, 酣, 醒, 睡, 拚, 宴,\n",
      "Nearest to 云: 缥, 雾, 烟, 旆, 霞, 颅, 纣, 姗,\n",
      "Nearest to 何: 岂, 邂, 否, 焉, 多, 那, 不, 昨,\n",
      "Nearest to 未: 不, 已, 乍, 初, 欲, 先, 易, 才,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 把, 想, 摘, 观,\n",
      "Nearest to 天: 空, 快, 罨, 逍, 莹, 蠲, 云, 帝,\n",
      "Nearest to 如: 似, 鳏, 藁, 旱, 踌, 陀, 漠, 依,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 纣, 碧, 蹲, 麦,\n",
      "Nearest to 、: ，, （, 。, ；, 踊, 竺, 浸, 年,\n",
      "Nearest to 长: 永, 逢, 平, 跎, 殇, 当, 滚, 修,\n",
      "Nearest to 歌: 唱, 遏, 乐, 舞, 箫, 郢, 曲, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 葛,\n",
      "Nearest to 酒: 屠, 釂, 醑, 盏, 酿, 醪, 杯, 樽,\n",
      "Nearest to 此: 兹, 每, 今, 一, 我, 予, 良, 敧,\n",
      "Average loss at step  162000 :  3.846125012636185\n",
      "Average loss at step  164000 :  3.894355534672737\n",
      "Average loss at step  166000 :  3.9116684337854384\n",
      "Average loss at step  168000 :  3.8848625762462614\n",
      "Average loss at step  170000 :  3.938596972107887\n",
      "Nearest to 子: 牧, 跌, 乙, 姆, 鸪, 荛, 乐, 潞,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 奈, 总, 却, 荏,\n",
      "Nearest to 醉: 笑, 醒, 睡, 酣, 困, 醺, 饮, 羞,\n",
      "Nearest to 云: 颅, 烟, 缥, 旆, 霞, 杳, 霭, 山,\n",
      "Nearest to 何: 焉, 邂, 岂, 多, 甚, 那, 无, 昔,\n",
      "Nearest to 未: 不, 乍, 已, 初, 易, 难, 欲, 裛,\n",
      "Nearest to 看: 觑, 趁, 对, 听, 见, 任, 与, 向,\n",
      "Nearest to 天: 逍, 快, 帝, 罨, 霓, 蠲, 空, 庸,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 鳏, 旱, 佣, 陀,\n",
      "Nearest to 水: 江, 山, 涎, 颅, 波, 溪, 蹲, 纣,\n",
      "Nearest to 、: ，, 。, ；, （, 怕, 竺, 恐, 隔,\n",
      "Nearest to 长: 平, 永, 当, 殇, 逢, 滚, 昽, 遥,\n",
      "Nearest to 歌: 唱, 舞, 曲, 箫, 乐, 遏, 郢, 侑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 楚,\n",
      "Nearest to 酒: 屠, 盏, 醪, 醑, 酿, 釂, 杯, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 我, 幸, 予, 朅, 良,\n",
      "Average loss at step  172000 :  3.938015555024147\n",
      "Average loss at step  174000 :  3.8980010470151902\n",
      "Average loss at step  176000 :  3.923323868274689\n",
      "Average loss at step  178000 :  3.917120938897133\n",
      "Average loss at step  180000 :  3.8116814304590223\n",
      "Nearest to 子: 跌, 牧, 潞, 乙, 乐, 籍, 鸪, 荛,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 共, 却, 奈,\n",
      "Nearest to 醉: 笑, 酣, 饮, 睡, 醺, 醒, 须, 酒,\n",
      "Nearest to 云: 旆, 天, 雾, 烟, 缥, 颅, 杳, 缔,\n",
      "Nearest to 何: 无, 邂, 多, 岂, 焉, 不, 昨, 否,\n",
      "Nearest to 未: 不, 乍, 难, 易, 已, 初, 欲, 也,\n",
      "Nearest to 看: 觑, 趁, 见, 对, 听, 向, 把, 拂,\n",
      "Nearest to 天: 逍, 云, 快, 空, 山, 忡, 罨, 岿,\n",
      "Nearest to 如: 似, 奈, 藁, 鳏, 而, 成, 为, 佣,\n",
      "Nearest to 水: 江, 山, 波, 派, 蹲, 睥, 海, 泠,\n",
      "Nearest to 、: ，, 。, 添, （, 眨, 怜, 逶, ；,\n",
      "Nearest to 长: 永, 平, 逢, 当, 正, 常, 遥, 滚,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 郢, 曲, 侑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 头, 葛, 鄞,\n",
      "Nearest to 酒: 屠, 盏, 酿, 醑, 杯, 醺, 篘, 醪,\n",
      "Nearest to 此: 兹, 今, 每, 良, 幸, 何, 也, 我,\n",
      "Average loss at step  182000 :  3.864879091858864\n",
      "Average loss at step  184000 :  3.884820712685585\n",
      "Average loss at step  186000 :  3.8235582954883576\n",
      "Average loss at step  188000 :  3.836206857085228\n",
      "Average loss at step  190000 :  3.859482542335987\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 荛, 缵, 乙, 儿,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 总, 奈, 半,\n",
      "Nearest to 醉: 笑, 饮, 须, 醒, 酣, 睡, 拚, 困,\n",
      "Nearest to 云: 雾, 烟, 缥, 旆, 霞, 霭, 缔, 纣,\n",
      "Nearest to 何: 岂, 邂, 不, 焉, 否, 昨, 那, 多,\n",
      "Nearest to 未: 不, 已, 初, 乍, 欲, 先, 易, 裛,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 与, 阅, 呼, 摘,\n",
      "Nearest to 天: 快, 逍, 罨, 空, 云, 蠲, 帝, 莹,\n",
      "Nearest to 如: 似, 鳏, 藁, 旱, 依, 踌, 佣, 奈,\n",
      "Nearest to 水: 江, 山, 颅, 溪, 蹲, 纣, 碧, 睥,\n",
      "Nearest to 、: ，, 。, （, ；, 眨, 姤, 惘, 踊,\n",
      "Nearest to 长: 永, 平, 常, 跎, 殇, 逢, 当, 修,\n",
      "Nearest to 歌: 唱, 遏, 乐, 箫, 舞, 郢, 阕, 硬,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 阡, 熄, 楚, 头,\n",
      "Nearest to 酒: 屠, 釂, 盏, 醑, 杯, 酿, 斟, 醪,\n",
      "Nearest to 此: 兹, 每, 今, 一, 幸, 良, 我, 敧,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  192000 :  3.8567823321819303\n",
      "Average loss at step  194000 :  3.880729498267174\n",
      "Average loss at step  196000 :  3.9209465926885603\n",
      "Average loss at step  198000 :  3.8817150942087175\n",
      "Average loss at step  200000 :  3.942554604768753\n",
      "Nearest to 子: 牧, 跌, 乙, 荛, 乐, 姆, 鸪, 戌,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 奈, 荏, 却,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 酣, 困, 饮, 须,\n",
      "Nearest to 云: 颅, 烟, 缥, 霞, 旆, 霭, 髭, 杳,\n",
      "Nearest to 何: 邂, 多, 焉, 岂, 昨, 否, 那, 昔,\n",
      "Nearest to 未: 不, 乍, 已, 易, 初, 也, 欲, 顿,\n",
      "Nearest to 看: 觑, 趁, 见, 对, 听, 任, 向, 摘,\n",
      "Nearest to 天: 逍, 快, 罨, 蠲, 春, 淝, 璀, 帝,\n",
      "Nearest to 如: 似, 奈, 藁, 鳏, 漠, 踌, 伧, 鷁,\n",
      "Nearest to 水: 江, 颅, 山, 涎, 溪, 波, 蹲, 派,\n",
      "Nearest to 、: ，, 。, ；, （, 怕, 竺, 说, 得,\n",
      "Nearest to 长: 平, 永, 当, 殇, 逢, 滚, 昽, 修,\n",
      "Nearest to 歌: 唱, 舞, 乐, 曲, 箫, 侑, 遏, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 楚,\n",
      "Nearest to 酒: 屠, 盏, 酿, 醑, 杯, 釂, 醪, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 我, 幸, 朅, 良, 涪,\n",
      "Average loss at step  202000 :  3.926718273282051\n",
      "Average loss at step  204000 :  3.894439533352852\n",
      "Average loss at step  206000 :  3.9246675354242324\n",
      "Average loss at step  208000 :  3.909209404349327\n",
      "Average loss at step  210000 :  3.812238497495651\n",
      "Nearest to 子: 跌, 牧, 潞, 乙, 籍, 乐, 鸪, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 共, 总, 奈, 取,\n",
      "Nearest to 醉: 笑, 醒, 饮, 睡, 酣, 醺, 宴, 拚,\n",
      "Nearest to 云: 缥, 旆, 雾, 烟, 颅, 霞, 天, 杳,\n",
      "Nearest to 何: 岂, 无, 邂, 多, 焉, 不, 否, 昨,\n",
      "Nearest to 未: 不, 难, 乍, 易, 欲, 已, 初, 也,\n",
      "Nearest to 看: 觑, 趁, 见, 向, 听, 把, 摘, 对,\n",
      "Nearest to 天: 逍, 云, 快, 罨, 空, 忡, 自, 庸,\n",
      "Nearest to 如: 似, 藁, 奈, 鳏, 成, 旱, 。, 为,\n",
      "Nearest to 水: 江, 波, 山, 派, 睥, 蹲, 涎, 泠,\n",
      "Nearest to 、: ，, 。, 逶, （, ；, 眨, 怜, 添,\n",
      "Nearest to 长: 永, 平, 常, 逢, 当, 遥, 正, 跎,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 侑, 曲, 柑,\n",
      "Nearest to 南: 北, 西, 阡, 东, 头, 熄, 葛, 鄞,\n",
      "Nearest to 酒: 屠, 酿, 盏, 醑, 釂, 杯, 醺, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 良, 幸, 旧, 我, 也,\n",
      "Average loss at step  212000 :  3.8622139196395873\n",
      "Average loss at step  214000 :  3.87444104886055\n",
      "Average loss at step  216000 :  3.8226529171466828\n",
      "Average loss at step  218000 :  3.8242709290385246\n",
      "Average loss at step  220000 :  3.8710185750722883\n",
      "Nearest to 子: 牧, 跌, 鸪, 荛, 潞, 姆, 乙, 瑾,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 奈, 共, 取,\n",
      "Nearest to 醉: 笑, 饮, 须, 醒, 睡, 酣, 拚, 困,\n",
      "Nearest to 云: 雾, 烟, 旆, 缥, 霞, 缔, 天, 颅,\n",
      "Nearest to 何: 岂, 邂, 多, 否, 焉, 不, 甚, 昨,\n",
      "Nearest to 未: 不, 初, 已, 乍, 易, 先, 欲, 才,\n",
      "Nearest to 看: 觑, 见, 趁, 把, 听, 想, 观, 阅,\n",
      "Nearest to 天: 云, 快, 逍, 罨, 空, 蠲, 帝, 怡,\n",
      "Nearest to 如: 似, 藁, 鳏, 旱, 踌, 佣, 依, 胥,\n",
      "Nearest to 水: 江, 山, 溪, 颅, 睥, 蹲, 纣, 碧,\n",
      "Nearest to 、: ，, 。, ；, （, 踊, 酤, 惘, 崆,\n",
      "Nearest to 长: 永, 平, 殇, 跎, 当, 逢, 远, 常,\n",
      "Nearest to 歌: 唱, 箫, 乐, 遏, 舞, 郢, 咽, 曲,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 楚,\n",
      "Nearest to 酒: 屠, 醑, 盏, 釂, 酿, 杯, 醪, 斟,\n",
      "Nearest to 此: 兹, 每, 今, 良, 敧, 一, 毋, 予,\n",
      "Average loss at step  222000 :  3.8574869997501375\n",
      "Average loss at step  224000 :  3.8794693590402605\n",
      "Average loss at step  226000 :  3.9308682128190995\n",
      "Average loss at step  228000 :  3.8899234496355057\n",
      "Average loss at step  230000 :  3.939725068807602\n",
      "Nearest to 子: 牧, 跌, 姆, 荛, 乙, 乐, 戌, 瞒,\n",
      "Nearest to 尽: 了, 遍, 都, 奈, 竟, 总, 荏, 半,\n",
      "Nearest to 醉: 笑, 醒, 睡, 醺, 酣, 困, 饮, 拚,\n",
      "Nearest to 云: 颅, 烟, 缥, 旆, 霭, 霞, 杳, 电,\n",
      "Nearest to 何: 焉, 邂, 岂, 还, 昨, 否, 那, 昔,\n",
      "Nearest to 未: 不, 乍, 已, 初, 易, 欲, 难, 也,\n",
      "Nearest to 看: 觑, 趁, 对, 任, 听, 摘, 见, 向,\n",
      "Nearest to 天: 逍, 快, 罨, 蠲, 璀, 帝, 莹, 淝,\n",
      "Nearest to 如: 似, 奈, 漠, 藁, 鳏, 踌, 胥, 碗,\n",
      "Nearest to 水: 江, 颅, 山, 涎, 蹲, 派, 淡, 漠,\n",
      "Nearest to 、: ，, 。, 竺, （, ；, 怕, 檎, 伴,\n",
      "Nearest to 长: 平, 永, 当, 逢, 昽, 殇, 滚, 遥,\n",
      "Nearest to 歌: 唱, 舞, 曲, 乐, 遏, 箫, 侑, 郢,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 熄, 涪,\n",
      "Nearest to 酒: 屠, 盏, 醪, 酿, 醑, 杯, 釂, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 良, 我, 一, 予, 幸,\n",
      "Average loss at step  232000 :  3.919221510052681\n",
      "Average loss at step  234000 :  3.8873602871894835\n",
      "Average loss at step  236000 :  3.914585928797722\n",
      "Average loss at step  238000 :  3.906673664212227\n",
      "Average loss at step  240000 :  3.8154054930210113\n",
      "Nearest to 子: 跌, 牧, 潞, 乐, 乙, 鸪, 籍, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 总, 取, 渐, 共,\n",
      "Nearest to 醉: 笑, 睡, 醒, 饮, 醺, 酣, 尊, 宴,\n",
      "Nearest to 云: 雾, 烟, 旆, 缥, 杳, 天, 颅, 缔,\n",
      "Nearest to 何: 无, 邂, 岂, 多, 昨, 焉, 否, 还,\n",
      "Nearest to 未: 不, 乍, 难, 易, 欲, 已, 初, 顿,\n",
      "Nearest to 看: 觑, 见, 趁, 向, 听, 摘, 拂, 把,\n",
      "Nearest to 天: 逍, 快, 云, 庸, 罨, 空, 忡, 骘,\n",
      "Nearest to 如: 似, 鳏, 藁, 奈, 成, 佣, 漠, 在,\n",
      "Nearest to 水: 江, 山, 派, 波, 蹲, 泠, 睥, 溪,\n",
      "Nearest to 、: ，, 。, 逶, ；, （, 姤, 添, 眨,\n",
      "Nearest to 长: 永, 平, 逢, 当, 滚, 常, 正, 遥,\n",
      "Nearest to 歌: 唱, 箫, 乐, 舞, 遏, 侑, 曲, 柑,\n",
      "Nearest to 南: 西, 北, 东, 阡, 熄, 湖, 葛, 鄞,\n",
      "Nearest to 酒: 屠, 酿, 醑, 釂, 醺, 盏, 饤, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 旧, 良, 难, 幸, 何,\n",
      "Average loss at step  242000 :  3.8573805220127104\n",
      "Average loss at step  244000 :  3.8789059839248656\n",
      "Average loss at step  246000 :  3.823187179207802\n",
      "Average loss at step  248000 :  3.829245007276535\n",
      "Average loss at step  250000 :  3.857207206130028\n",
      "Nearest to 子: 牧, 跌, 鸪, 荛, 潞, 乙, 缵, 儿,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 半, 奈, 败,\n",
      "Nearest to 醉: 笑, 须, 饮, 睡, 拚, 醒, 酣, 宴,\n",
      "Nearest to 云: 烟, 雾, 旆, 缥, 霞, 纣, 缔, 颅,\n",
      "Nearest to 何: 岂, 多, 邂, 不, 否, 甚, 焉, 那,\n",
      "Nearest to 未: 不, 已, 初, 乍, 欲, 易, 先, 也,\n",
      "Nearest to 看: 觑, 见, 趁, 把, 听, 想, 阅, 摘,\n",
      "Nearest to 天: 快, 逍, 罨, 蠲, 怡, 空, 帝, 岿,\n",
      "Nearest to 如: 似, 鳏, 藁, 旱, 踌, 佣, 依, 胥,\n",
      "Nearest to 水: 江, 山, 蹲, 颅, 溪, 碧, 纣, 派,\n",
      "Nearest to 、: ，, 。, （, ；, 得, 逶, 眨, 酤,\n",
      "Nearest to 长: 永, 平, 殇, 当, 逢, 跎, 修, 常,\n",
      "Nearest to 歌: 唱, 乐, 遏, 箫, 舞, 阕, 咽, 曲,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 阡, 熄, 楚, 头,\n",
      "Nearest to 酒: 屠, 盏, 釂, 醑, 醪, 酿, 杯, 斟,\n",
      "Nearest to 此: 兹, 每, 今, 一, 幸, 何, 敧, 良,\n",
      "Average loss at step  252000 :  3.8677639335393907\n",
      "Average loss at step  254000 :  3.902131943941116\n",
      "Average loss at step  256000 :  3.911086846709251\n",
      "Average loss at step  258000 :  3.899152615785599\n",
      "Average loss at step  260000 :  3.934999990105629\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 荛, 戌, 乐, 瞒,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 奈, 总, 荏, 却,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 困, 酣, 拚, 羞,\n",
      "Nearest to 云: 颅, 烟, 缥, 霭, 霞, 雾, 髭, 旆,\n",
      "Nearest to 何: 邂, 焉, 多, 岂, 否, 甚, 那, 还,\n",
      "Nearest to 未: 不, 乍, 已, 易, 初, 难, 欲, 顿,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 任, 见, 想, 向,\n",
      "Nearest to 天: 逍, 快, 罨, 蠲, 己, 帝, 淝, 云,\n",
      "Nearest to 如: 似, 奈, 漠, 鳏, 藁, 踌, 虽, 旱,\n",
      "Nearest to 水: 江, 颅, 山, 派, 溪, 涎, 蹲, 碧,\n",
      "Nearest to 、: ，, 。, ；, 怕, （, 寞, 浸, 恐,\n",
      "Nearest to 长: 永, 平, 当, 滚, 殇, 逢, 昽, 修,\n",
      "Nearest to 歌: 唱, 舞, 遏, 箫, 曲, 乐, 郢, 侑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 熄, 横,\n",
      "Nearest to 酒: 屠, 盏, 酿, 醪, 釂, 醑, 杯, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 良, 我, 涪, 也, 何,\n",
      "Average loss at step  262000 :  3.9064053212404253\n",
      "Average loss at step  264000 :  3.8936930198669435\n",
      "Average loss at step  266000 :  3.907570215702057\n",
      "Average loss at step  268000 :  3.876015069961548\n",
      "Average loss at step  270000 :  3.841893853187561\n",
      "Nearest to 子: 跌, 牧, 潞, 乙, 鸪, 籍, 乐, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 总, 共, 渐, 取,\n",
      "Nearest to 醉: 笑, 睡, 醺, 醒, 饮, 须, 酣, 尊,\n",
      "Nearest to 云: 烟, 雾, 旆, 缥, 缔, 天, 杳, 颅,\n",
      "Nearest to 何: 邂, 昨, 无, 多, 岂, 不, 焉, 否,\n",
      "Nearest to 未: 不, 乍, 难, 欲, 易, 初, 已, 顿,\n",
      "Nearest to 看: 觑, 趁, 见, 向, 听, 摘, 把, 想,\n",
      "Nearest to 天: 逍, 云, 快, 忡, 空, 罨, 庸, 怡,\n",
      "Nearest to 如: 似, 奈, 藁, 鳏, 旱, 佣, 漠, 为,\n",
      "Nearest to 水: 江, 山, 波, 派, 蹲, 睥, 碧, 涎,\n",
      "Nearest to 、: ，, 。, 逶, （, 姤, ；, 惘, 酤,\n",
      "Nearest to 长: 永, 平, 滚, 逢, 常, 遥, 当, 远,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 侑, 曲, 柑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 葛, 湖, 鄞,\n",
      "Nearest to 酒: 屠, 酿, 醺, 釂, 醪, 醑, 盏, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 一, 良, 难, 幸, 何,\n",
      "Average loss at step  272000 :  3.8497575163841247\n",
      "Average loss at step  274000 :  3.871978004336357\n",
      "Average loss at step  276000 :  3.8228261443376543\n",
      "Average loss at step  278000 :  3.8270981850028036\n",
      "Average loss at step  280000 :  3.8492444363832474\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 儿, 荛, 乙, 姆,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 败, 渐, 总, 外,\n",
      "Nearest to 醉: 笑, 须, 睡, 饮, 酣, 醒, 拚, 困,\n",
      "Nearest to 云: 烟, 雾, 缥, 霞, 旆, 杳, 缔, 颅,\n",
      "Nearest to 何: 邂, 岂, 多, 甚, 不, 焉, 那, 否,\n",
      "Nearest to 未: 不, 已, 乍, 初, 易, 先, 难, 欲,\n",
      "Nearest to 看: 觑, 见, 趁, 想, 听, 与, 摘, 把,\n",
      "Nearest to 天: 罨, 快, 空, 逍, 蠲, 莹, 云, 朗,\n",
      "Nearest to 如: 似, 藁, 鳏, 旱, 奈, 依, 践, 踌,\n",
      "Nearest to 水: 江, 山, 蹲, 碧, 颅, 纣, 麦, 派,\n",
      "Nearest to 、: ，, 。, ；, （, 眨, 得, 恐, 似,\n",
      "Nearest to 长: 永, 殇, 当, 平, 逢, 跎, 常, 正,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 舞, 阕, 曲, 咽,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 楚, 头,\n",
      "Nearest to 酒: 屠, 斟, 釂, 醑, 盏, 酿, 醪, 杯,\n",
      "Nearest to 此: 兹, 每, 今, 一, 何, 我, 予, 幸,\n",
      "Average loss at step  282000 :  3.8782616449594496\n",
      "Average loss at step  284000 :  3.9126347968578337\n",
      "Average loss at step  286000 :  3.906076747775078\n",
      "Average loss at step  288000 :  3.9028412381410598\n",
      "Average loss at step  290000 :  3.931700474977493\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 荛, 乐, 儿, 鸪,\n",
      "Nearest to 尽: 了, 遍, 都, 奈, 竟, 总, 荏, 半,\n",
      "Nearest to 醉: 笑, 醒, 睡, 醺, 酣, 困, 饮, 酒,\n",
      "Nearest to 云: 烟, 霞, 颅, 缥, 霭, 雾, 山, 髭,\n",
      "Nearest to 何: 邂, 焉, 多, 还, 岂, 那, 昨, 否,\n",
      "Nearest to 未: 不, 乍, 已, 初, 易, 欲, 难, 也,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 任, 想, 向, 见,\n",
      "Nearest to 天: 逍, 快, 罨, 蠲, 淝, 帝, 霓, 己,\n",
      "Nearest to 如: 似, 奈, 藁, 鳏, 漠, 踌, 旱, 凛,\n",
      "Nearest to 水: 江, 颅, 山, 波, 涎, 派, 渚, 溪,\n",
      "Nearest to 、: ，, 。, （, ；, 怕, 竺, 檎, 隔,\n",
      "Nearest to 长: 平, 永, 当, 逢, 殇, 滚, 遥, 昽,\n",
      "Nearest to 歌: 唱, 舞, 箫, 遏, 乐, 曲, 郢, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 熄, 涪,\n",
      "Nearest to 酒: 屠, 盏, 酿, 杯, 釂, 醪, 蘋, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 良, 也, 幸, 之, 予,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  292000 :  3.895756785273552\n",
      "Average loss at step  294000 :  3.900539903998375\n",
      "Average loss at step  296000 :  3.9049380631446837\n",
      "Average loss at step  298000 :  3.870839011788368\n",
      "Average loss at step  300000 :  3.8329764723777773\n",
      "Nearest to 子: 跌, 牧, 潞, 乙, 鸪, 籍, 姆, 缵,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 共, 总, 取,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 酣, 饮, 拚, 宴,\n",
      "Nearest to 云: 烟, 雾, 旆, 缥, 杳, 天, 霞, 髭,\n",
      "Nearest to 何: 邂, 焉, 岂, 昨, 无, 多, 不, 否,\n",
      "Nearest to 未: 不, 乍, 难, 欲, 易, 已, 初, 也,\n",
      "Nearest to 看: 觑, 趁, 见, 想, 向, 摘, 听, 把,\n",
      "Nearest to 天: 逍, 云, 罨, 怡, 快, 忡, 庸, 霓,\n",
      "Nearest to 如: 似, 鳏, 奈, 藁, 为, 佣, 旱, 而,\n",
      "Nearest to 水: 江, 山, 派, 波, 蹲, 涎, 睥, 泠,\n",
      "Nearest to 、: ，, 。, （, ；, 逶, 酤, 姤, 逐,\n",
      "Nearest to 长: 永, 平, 滚, 常, 当, 逢, 遥, 远,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 遏, 曲, 柑, 侑,\n",
      "Nearest to 南: 西, 北, 东, 阡, 熄, 鄞, 楚, 湖,\n",
      "Nearest to 酒: 屠, 酿, 醑, 釂, 醺, 杯, 醪, 盏,\n",
      "Nearest to 此: 兹, 今, 每, 旧, 良, 幸, 毋, 怎,\n",
      "Average loss at step  302000 :  3.8626077789068223\n",
      "Average loss at step  304000 :  3.8458979902267454\n",
      "Average loss at step  306000 :  3.8265889377593996\n",
      "Average loss at step  308000 :  3.8286396819353103\n",
      "Average loss at step  310000 :  3.857289287447929\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 荛, 姆, 缵, 儿,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 渐, 奈, 败, 总,\n",
      "Nearest to 醉: 笑, 须, 饮, 拚, 睡, 宴, 釂, 酣,\n",
      "Nearest to 云: 烟, 雾, 缥, 霞, 旆, 纣, 杳, 缔,\n",
      "Nearest to 何: 岂, 多, 邂, 不, 焉, 否, 此, 那,\n",
      "Nearest to 未: 不, 已, 先, 乍, 易, 初, 欲, 难,\n",
      "Nearest to 看: 觑, 趁, 见, 想, 把, 听, 阅, 观,\n",
      "Nearest to 天: 罨, 快, 逍, 空, 蠲, 茉, 莹, 淝,\n",
      "Nearest to 如: 似, 藁, 鳏, 依, 旱, 踌, 胥, 佣,\n",
      "Nearest to 水: 江, 山, 颅, 纣, 睥, 蹲, 派, 溪,\n",
      "Nearest to 、: ，, 。, ；, （, 似, 酤, 眨, 恁,\n",
      "Nearest to 长: 永, 殇, 平, 当, 跎, 逢, 常, 滚,\n",
      "Nearest to 歌: 唱, 乐, 遏, 箫, 舞, 曲, 咽, 郢,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 楚, 头,\n",
      "Nearest to 酒: 屠, 釂, 盏, 斟, 醪, 醑, 杯, 醺,\n",
      "Nearest to 此: 兹, 每, 今, 一, 何, 朅, 幸, 良,\n",
      "Average loss at step  312000 :  3.871953753352165\n",
      "Average loss at step  314000 :  3.9234600533246993\n",
      "Average loss at step  316000 :  3.8924451044797896\n",
      "Average loss at step  318000 :  3.915354008436203\n",
      "Average loss at step  320000 :  3.9178192285299303\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 荛, 乐, 戌, 潞,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 奈, 总, 荏, 却,\n",
      "Nearest to 醉: 笑, 醒, 睡, 醺, 酣, 饮, 困, 拚,\n",
      "Nearest to 云: 颅, 烟, 霞, 缥, 霭, 雾, 山, 花,\n",
      "Nearest to 何: 多, 焉, 邂, 那, 岂, 甚, 否, 昨,\n",
      "Nearest to 未: 不, 乍, 初, 已, 易, 顿, 裛, 也,\n",
      "Nearest to 看: 觑, 趁, 对, 任, 向, 见, 听, 想,\n",
      "Nearest to 天: 逍, 快, 帝, 蠲, 罨, 淝, 霓, 己,\n",
      "Nearest to 如: 似, 奈, 漠, 藁, 鳏, 溅, 旱, 鷁,\n",
      "Nearest to 水: 江, 山, 颅, 蹲, 涎, 派, 泠, 渚,\n",
      "Nearest to 、: ，, 。, （, 竺, 怕, ；, 说, 檎,\n",
      "Nearest to 长: 平, 永, 滚, 当, 殇, 逢, 阊, 遥,\n",
      "Nearest to 歌: 唱, 舞, 箫, 遏, 曲, 郢, 乐, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 头, 涪, 吴,\n",
      "Nearest to 酒: 屠, 盏, 酿, 蘋, 醪, 杯, 釂, 醺,\n",
      "Nearest to 此: 兹, 今, 每, 良, 予, 我, 幸, 何,\n",
      "Average loss at step  322000 :  3.891810502290726\n",
      "Average loss at step  324000 :  3.9054523823261262\n",
      "Average loss at step  326000 :  3.908110317468643\n",
      "Average loss at step  328000 :  3.843985190272331\n",
      "Average loss at step  330000 :  3.845147893548012\n",
      "Nearest to 子: 跌, 牧, 潞, 鸪, 乙, 乐, 姆, 籍,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 总, 共, 取,\n",
      "Nearest to 醉: 笑, 睡, 饮, 醺, 醒, 酣, 拚, 须,\n",
      "Nearest to 云: 雾, 烟, 旆, 缥, 霞, 杳, 天, 颅,\n",
      "Nearest to 何: 邂, 岂, 不, 无, 昨, 焉, 多, 否,\n",
      "Nearest to 未: 不, 乍, 难, 初, 欲, 易, 先, 也,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 向, 听, 把, 想,\n",
      "Nearest to 天: 逍, 云, 罨, 空, 快, 骘, 怡, 忡,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 为, 佣, 漠, 成,\n",
      "Nearest to 水: 江, 派, 山, 波, 涎, 睥, 泠, 蹲,\n",
      "Nearest to 、: ，, 。, （, 酤, 眨, 添, 姤, 惘,\n",
      "Nearest to 长: 永, 平, 滚, 正, 逢, 远, 当, 常,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 遏, 曲, 侑, 柑,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 楚, 熄, 头,\n",
      "Nearest to 酒: 屠, 酿, 醑, 盏, 醺, 醪, 蘋, 釂,\n",
      "Nearest to 此: 兹, 今, 每, 良, 一, 幸, 旧, 毋,\n",
      "Average loss at step  332000 :  3.870958108663559\n",
      "Average loss at step  334000 :  3.834566880583763\n",
      "Average loss at step  336000 :  3.8254314712285997\n",
      "Average loss at step  338000 :  3.8376866313815117\n",
      "Average loss at step  340000 :  3.8484530671834944\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 荛, 姆, 儿, 缵,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 渐, 总, 败, 洒,\n",
      "Nearest to 醉: 笑, 睡, 饮, 须, 酣, 醺, 拚, 醒,\n",
      "Nearest to 云: 烟, 雾, 缥, 杳, 霞, 旆, 濛, 纣,\n",
      "Nearest to 何: 岂, 邂, 多, 否, 不, 那, 甚, 无,\n",
      "Nearest to 未: 不, 已, 易, 初, 乍, 先, 难, 欲,\n",
      "Nearest to 看: 觑, 见, 趁, 想, 摘, 听, 与, 把,\n",
      "Nearest to 天: 快, 空, 逍, 罨, 茉, 淝, 帝, 蠲,\n",
      "Nearest to 如: 似, 藁, 鳏, 胥, 旱, 佣, 踌, 践,\n",
      "Nearest to 水: 江, 山, 颅, 蹲, 碧, 纣, 麦, 睥,\n",
      "Nearest to 、: ，, 。, ；, （, 竺, 似, 惘, 姤,\n",
      "Nearest to 长: 永, 殇, 平, 当, 跎, 逢, 亶, 常,\n",
      "Nearest to 歌: 唱, 乐, 遏, 箫, 舞, 曲, 咽, 阕,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 阡, 熄, 头, 楚,\n",
      "Nearest to 酒: 屠, 釂, 醑, 盏, 斟, 醪, 酿, 杯,\n",
      "Nearest to 此: 兹, 每, 今, 何, 一, 幸, 朅, 敧,\n",
      "Average loss at step  342000 :  3.8834382829666136\n",
      "Average loss at step  344000 :  3.917822976589203\n",
      "Average loss at step  346000 :  3.88709110224247\n",
      "Average loss at step  348000 :  3.933339443564415\n",
      "Average loss at step  350000 :  3.911507952213287\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 乐, 潞, 荛, 戌,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 奈, 半, 荏,\n",
      "Nearest to 醉: 笑, 醺, 睡, 醒, 酣, 饮, 困, 皴,\n",
      "Nearest to 云: 颅, 烟, 缥, 霭, 霞, 雾, 髭, 山,\n",
      "Nearest to 何: 邂, 焉, 多, 还, 岂, 否, 那, 甚,\n",
      "Nearest to 未: 不, 乍, 已, 初, 易, 顿, 也, 裛,\n",
      "Nearest to 看: 觑, 趁, 对, 任, 想, 向, 见, 听,\n",
      "Nearest to 天: 快, 逍, 淝, 罨, 蠲, 忡, 春, 帝,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 鳏, 溅, 旱, 娜,\n",
      "Nearest to 水: 江, 山, 颅, 蹲, 涎, 派, 波, 溪,\n",
      "Nearest to 、: ，, 。, （, ；, 怕, 恐, 檎, 拣,\n",
      "Nearest to 长: 平, 永, 滚, 遥, 逢, 当, 殇, 阊,\n",
      "Nearest to 歌: 唱, 舞, 箫, 乐, 遏, 曲, 郢, 吟,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 阡, 头, 熄, 涪,\n",
      "Nearest to 酒: 屠, 盏, 酿, 杯, 蘋, 釂, 醑, 醪,\n",
      "Nearest to 此: 兹, 今, 每, 良, 幸, 我, 予, 何,\n",
      "Average loss at step  352000 :  3.8982937710285186\n",
      "Average loss at step  354000 :  3.8991358622312546\n",
      "Average loss at step  356000 :  3.9098887964487075\n",
      "Average loss at step  358000 :  3.820826115131378\n",
      "Average loss at step  360000 :  3.8609214611053466\n",
      "Nearest to 子: 跌, 牧, 潞, 鸪, 乙, 籍, 乐, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 总, 渐, 取, 奈,\n",
      "Nearest to 醉: 笑, 醺, 睡, 醒, 酣, 饮, 宴, 猛,\n",
      "Nearest to 云: 烟, 雾, 旆, 霞, 缥, 天, 杳, 霭,\n",
      "Nearest to 何: 邂, 不, 无, 岂, 焉, 昨, 多, 否,\n",
      "Nearest to 未: 不, 乍, 欲, 难, 易, 已, 初, 顿,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 听, 向, 对, 想,\n",
      "Nearest to 天: 云, 罨, 逍, 空, 快, 淝, 霄, 月,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 佣, 成, 为, 漠,\n",
      "Nearest to 水: 江, 山, 派, 睥, 波, 蹲, 涎, 溪,\n",
      "Nearest to 、: ，, 。, （, ；, 姤, 酤, 逶, 逐,\n",
      "Nearest to 长: 永, 平, 逢, 滚, 当, 正, 远, 常,\n",
      "Nearest to 歌: 唱, 乐, 舞, 箫, 曲, 遏, 柑, 硬,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 楚, 湖,\n",
      "Nearest to 酒: 屠, 酿, 醑, 醺, 盏, 醪, 釂, 杯,\n",
      "Nearest to 此: 兹, 今, 每, 良, 幸, 我, 一, 其,\n",
      "Average loss at step  362000 :  3.8732109529972076\n",
      "Average loss at step  364000 :  3.8247831548452376\n",
      "Average loss at step  366000 :  3.827022708296776\n",
      "Average loss at step  368000 :  3.8567419064044954\n",
      "Average loss at step  370000 :  3.840838403224945\n",
      "Nearest to 子: 牧, 跌, 鸪, 潞, 姆, 乙, 荛, 籍,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 奈, 半, 洒, 败,\n",
      "Nearest to 醉: 笑, 饮, 睡, 拚, 醒, 须, 醺, 酣,\n",
      "Nearest to 云: 雾, 烟, 缥, 霞, 杳, 瑛, 缔, 纣,\n",
      "Nearest to 何: 邂, 岂, 那, 多, 否, 不, 焉, 此,\n",
      "Nearest to 未: 不, 已, 乍, 易, 才, 欲, 初, 先,\n",
      "Nearest to 看: 觑, 见, 趁, 想, 与, 摘, 把, 听,\n",
      "Nearest to 天: 快, 逍, 罨, 空, 茉, 淝, 蠲, 岿,\n",
      "Nearest to 如: 似, 藁, 鳏, 佣, 踌, 旱, 胥, 奈,\n",
      "Nearest to 水: 江, 颅, 蹲, 山, 纣, 溪, 稼, 碧,\n",
      "Nearest to 、: ，, 。, ；, （, 说, 恐, 怕, 窬,\n",
      "Nearest to 长: 永, 殇, 当, 平, 跎, 逢, 滚, 修,\n",
      "Nearest to 歌: 唱, 遏, 乐, 箫, 舞, 曲, 咽, 郢,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 阡, 熄, 头, 楚,\n",
      "Nearest to 酒: 屠, 釂, 醑, 酿, 斟, 醪, 盏, 杯,\n",
      "Nearest to 此: 兹, 每, 今, 一, 幸, 何, 朅, 敧,\n",
      "Average loss at step  372000 :  3.8821270138025286\n",
      "Average loss at step  374000 :  3.9167530987262724\n",
      "Average loss at step  376000 :  3.8783041359186172\n",
      "Average loss at step  378000 :  3.9250397572517395\n",
      "Average loss at step  380000 :  3.924302591919899\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 乐, 潞, 荛, 鸪,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 却, 奈, 半,\n",
      "Nearest to 醉: 笑, 醒, 醺, 睡, 酣, 饮, 困, 掏,\n",
      "Nearest to 云: 颅, 烟, 髭, 霞, 花, 雾, 霭, 山,\n",
      "Nearest to 何: 多, 邂, 那, 焉, 否, 还, 甚, 底,\n",
      "Nearest to 未: 不, 乍, 初, 易, 已, 顿, 欲, 也,\n",
      "Nearest to 看: 觑, 趁, 对, 见, 向, 任, 想, 摘,\n",
      "Nearest to 天: 快, 逍, 云, 淝, 罨, 山, 帝, 蠲,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 漠, 而, 溅, 旱,\n",
      "Nearest to 水: 江, 山, 颅, 波, 蹲, 涎, 溪, 派,\n",
      "Nearest to 、: ，, 。, 是, 说, 似, 酤, 檎, 拣,\n",
      "Nearest to 长: 平, 永, 当, 殇, 滚, 阊, 修, 昽,\n",
      "Nearest to 歌: 唱, 舞, 遏, 箫, 乐, 阕, 郢, 曲,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 头, 熄, 吴,\n",
      "Nearest to 酒: 屠, 盏, 釂, 酿, 蘋, 杯, 醪, 醑,\n",
      "Nearest to 此: 兹, 今, 每, 良, 我, 何, 予, 幸,\n",
      "Average loss at step  382000 :  3.8948717885017397\n",
      "Average loss at step  384000 :  3.8995816305875777\n",
      "Average loss at step  386000 :  3.907418883562088\n",
      "Average loss at step  388000 :  3.8103850210905077\n",
      "Average loss at step  390000 :  3.8478511877059938\n",
      "Nearest to 子: 牧, 跌, 潞, 籍, 鸪, 乙, 乐, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 总, 渐, 半, 奈,\n",
      "Nearest to 醉: 笑, 醺, 睡, 醒, 酣, 饮, 须, 困,\n",
      "Nearest to 云: 烟, 雾, 霞, 旆, 缥, 天, 颅, 杳,\n",
      "Nearest to 何: 无, 不, 邂, 岂, 焉, 昨, 多, 乎,\n",
      "Nearest to 未: 不, 乍, 易, 欲, 难, 已, 顿, 初,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 听, 想, 向, 对,\n",
      "Nearest to 天: 云, 罨, 空, 逍, 淝, 霄, 快, 自,\n",
      "Nearest to 如: 似, 鳏, 藁, 奈, 漠, 佣, 截, 成,\n",
      "Nearest to 水: 山, 江, 派, 波, 睥, 涎, 溪, 泠,\n",
      "Nearest to 、: ，, 。, （, ；, 逶, 酤, 轣, 逐,\n",
      "Nearest to 长: 永, 平, 逢, 远, 滚, 遥, 常, 跎,\n",
      "Nearest to 歌: 唱, 乐, 箫, 舞, 遏, 曲, 吟, 硬,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 楚, 熄,\n",
      "Nearest to 酒: 屠, 酿, 醑, 醪, 釂, 盏, 醺, 谪,\n",
      "Nearest to 此: 兹, 今, 每, 良, 幸, 其, 毋, 我,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  392000 :  3.883377966403961\n",
      "Average loss at step  394000 :  3.8216588114500047\n",
      "Average loss at step  396000 :  3.8277689592838287\n",
      "Average loss at step  398000 :  3.8532012892365457\n",
      "Average loss at step  400000 :  3.8423763275146485\n",
      "Nearest to 子: 牧, 跌, 潞, 鸪, 乙, 儿, 缵, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 半, 败, 奈, 渐,\n",
      "Nearest to 醉: 笑, 饮, 醒, 睡, 宴, 须, 拚, 酣,\n",
      "Nearest to 云: 雾, 烟, 缥, 颅, 缔, 霞, 瑛, 杳,\n",
      "Nearest to 何: 邂, 多, 岂, 那, 否, 焉, 甚, 乎,\n",
      "Nearest to 未: 不, 已, 易, 才, 乍, 欲, 难, 也,\n",
      "Nearest to 看: 觑, 见, 趁, 想, 与, 听, 摘, 把,\n",
      "Nearest to 天: 快, 逍, 罨, 茉, 淝, 空, 岿, 蠲,\n",
      "Nearest to 如: 似, 藁, 胥, 佣, 鳏, 践, 踌, 虽,\n",
      "Nearest to 水: 江, 颅, 蹲, 碧, 纣, 稼, 溪, 麦,\n",
      "Nearest to 、: ，, 。, （, ；, 檎, 竺, ：, 说,\n",
      "Nearest to 长: 永, 殇, 平, 当, 逢, 修, 滚, 跎,\n",
      "Nearest to 歌: 唱, 遏, 乐, 舞, 箫, 曲, 硬, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 熄, 头, 柂,\n",
      "Nearest to 酒: 屠, 釂, 酿, 斟, 醑, 盏, 醪, 杯,\n",
      "Nearest to 此: 兹, 每, 今, 一, 幸, 朅, 何, 其,\n",
      "Average loss at step  402000 :  3.8862692304849626\n",
      "Average loss at step  404000 :  3.9087333631515504\n",
      "Average loss at step  406000 :  3.8766881675720213\n",
      "Average loss at step  408000 :  3.931821484446526\n",
      "Average loss at step  410000 :  3.925769334077835\n",
      "Nearest to 子: 跌, 牧, 姆, 乙, 乐, 潞, 荛, 瞒,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 奈, 总, 却, 半,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 酣, 困, 须, 饮,\n",
      "Nearest to 云: 颅, 烟, 髭, 霞, 霭, 汛, 雾, 雨,\n",
      "Nearest to 何: 多, 邂, 焉, 那, 否, 昨, 还, 岂,\n",
      "Nearest to 未: 不, 乍, 易, 顿, 已, 初, 难, 裛,\n",
      "Nearest to 看: 觑, 趁, 对, 听, 向, 观, 见, 任,\n",
      "Nearest to 天: 快, 逍, 罨, 淝, 云, 帝, 自, 春,\n",
      "Nearest to 如: 似, 奈, 鳏, 漠, 藁, 而, 旱, 溅,\n",
      "Nearest to 水: 江, 山, 颅, 波, 溪, 澹, 派, 蹲,\n",
      "Nearest to 、: ，, 。, 檎, （, 说, 是, 入, 似,\n",
      "Nearest to 长: 平, 永, 当, 滚, 殇, 阊, 逢, 修,\n",
      "Nearest to 歌: 唱, 舞, 箫, 遏, 阕, 曲, 吟, 咽,\n",
      "Nearest to 南: 北, 西, 东, 头, 阡, 鄞, 涪, 熄,\n",
      "Nearest to 酒: 屠, 盏, 酿, 杯, 蘋, 釂, 醪, 斟,\n",
      "Nearest to 此: 兹, 今, 每, 何, 良, 我, 予, 幸,\n",
      "Average loss at step  412000 :  3.8898010371923446\n",
      "Average loss at step  414000 :  3.9085688571929933\n",
      "Average loss at step  416000 :  3.9046928640604017\n",
      "Average loss at step  418000 :  3.8060428677797318\n",
      "Average loss at step  420000 :  3.856928966283798\n",
      "Nearest to 子: 牧, 跌, 潞, 荛, 籍, 鸪, 乙, 姆,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 渐, 奈, 半, 总,\n",
      "Nearest to 醉: 笑, 醺, 醒, 睡, 酣, 饮, 困, 须,\n",
      "Nearest to 云: 烟, 雾, 天, 旆, 霞, 颅, 缥, 杳,\n",
      "Nearest to 何: 不, 邂, 无, 昨, 岂, 多, 焉, 否,\n",
      "Nearest to 未: 不, 乍, 欲, 易, 难, 先, 已, 裛,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 听, 对, 拂, 向,\n",
      "Nearest to 天: 云, 罨, 逍, 淝, 空, 快, 月, 霄,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 漠, 佣, 成, 溅,\n",
      "Nearest to 水: 江, 山, 派, 波, 睥, 涎, 蹲, 泠,\n",
      "Nearest to 、: ，, 。, （, 逐, 酤, 鞮, 送, 伴,\n",
      "Nearest to 长: 永, 平, 常, 滚, 当, 跎, 逢, 远,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 舞, 曲, 吟, 饮,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 熄, 楚,\n",
      "Nearest to 酒: 屠, 醑, 釂, 酿, 醪, 斟, 醺, 杯,\n",
      "Nearest to 此: 兹, 今, 每, 良, 何, 幸, 我, 怎,\n",
      "Average loss at step  422000 :  3.8700664098262787\n",
      "Average loss at step  424000 :  3.8163205963373183\n",
      "Average loss at step  426000 :  3.8109322392344476\n",
      "Average loss at step  428000 :  3.868834533929825\n",
      "Average loss at step  430000 :  3.8541838495731353\n",
      "Nearest to 子: 跌, 牧, 潞, 鸪, 缵, 乙, 荛, 姆,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 半, 洒, 取, 奈,\n",
      "Nearest to 醉: 笑, 饮, 睡, 须, 醒, 醺, 拚, 宴,\n",
      "Nearest to 云: 雾, 烟, 缔, 缥, 瑛, 颅, 髭, 霭,\n",
      "Nearest to 何: 邂, 多, 岂, 那, 焉, 此, 否, 甚,\n",
      "Nearest to 未: 不, 已, 才, 乍, 易, 难, 初, 也,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 把, 摘, 想, 对,\n",
      "Nearest to 天: 快, 罨, 逍, 茉, 空, 蠲, 绚, 岿,\n",
      "Nearest to 如: 似, 藁, 胥, 鳏, 佣, 旱, 踌, 奈,\n",
      "Nearest to 水: 江, 蹲, 颅, 山, 纣, 稼, 麦, 碧,\n",
      "Nearest to 、: ，, 。, ；, （, 说, 竺, 怕, 到,\n",
      "Nearest to 长: 永, 平, 殇, 当, 跎, 逢, 滚, 修,\n",
      "Nearest to 歌: 唱, 遏, 乐, 箫, 曲, 硬, 阕, 舞,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 鄞, 柂, 头,\n",
      "Nearest to 酒: 屠, 釂, 酿, 醑, 斟, 盏, 醪, 谪,\n",
      "Nearest to 此: 兹, 每, 今, 朅, 幸, 何, 一, 怎,\n",
      "Average loss at step  432000 :  3.87471239733696\n",
      "Average loss at step  434000 :  3.91715265250206\n",
      "Average loss at step  436000 :  3.8728027950525283\n",
      "Average loss at step  438000 :  3.9422544413805007\n",
      "Average loss at step  440000 :  3.9137817816734315\n",
      "Nearest to 子: 牧, 跌, 姆, 乐, 乙, 荛, 潞, 儿,\n",
      "Nearest to 尽: 了, 遍, 都, 总, 竟, 奈, 却, 荏,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 酣, 须, 困, 羞,\n",
      "Nearest to 云: 颅, 霞, 髭, 烟, 霭, 雾, 缥, 纣,\n",
      "Nearest to 何: 多, 邂, 还, 焉, 否, 昨, 那, 岂,\n",
      "Nearest to 未: 不, 乍, 初, 已, 易, 顿, 也, 裛,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 想, 任, 见, 阅,\n",
      "Nearest to 天: 快, 逍, 罨, 淝, 帝, 云, 蠲, 霓,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 鳏, 溅, 而, 旱,\n",
      "Nearest to 水: 江, 波, 山, 溪, 颅, 派, 碛, 泉,\n",
      "Nearest to 、: ，, 。, 入, （, 浸, 檎, 是, 说,\n",
      "Nearest to 长: 平, 永, 当, 滚, 殇, 逢, 遥, 跎,\n",
      "Nearest to 歌: 唱, 舞, 遏, 箫, 乐, 曲, 阕, 硬,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 头, 熄, 涪,\n",
      "Nearest to 酒: 屠, 盏, 釂, 斟, 杯, 醑, 醺, 蘋,\n",
      "Nearest to 此: 兹, 今, 每, 良, 之, 予, 朅, 涪,\n",
      "Average loss at step  442000 :  3.8869031134843826\n",
      "Average loss at step  444000 :  3.905070924639702\n",
      "Average loss at step  446000 :  3.894187592506409\n",
      "Average loss at step  448000 :  3.8035984904766083\n",
      "Average loss at step  450000 :  3.8553690053224563\n",
      "Nearest to 子: 跌, 牧, 潞, 荛, 乐, 籍, 鸪, 姆,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 渐, 总, 断, 奈,\n",
      "Nearest to 醉: 笑, 醺, 醒, 睡, 酣, 饮, 须, 困,\n",
      "Nearest to 云: 烟, 雾, 颅, 天, 旆, 霞, 缥, 杳,\n",
      "Nearest to 何: 不, 邂, 岂, 昨, 无, 今, 焉, 多,\n",
      "Nearest to 未: 不, 乍, 欲, 难, 易, 已, 顿, 裛,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 摘, 拂, 想, 对,\n",
      "Nearest to 天: 云, 罨, 空, 逍, 淝, 快, 霄, 蠲,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 佣, 成, 漠, 而,\n",
      "Nearest to 水: 山, 江, 派, 涎, 蹲, 睥, 波, 溪,\n",
      "Nearest to 、: ，, 。, （, 酤, 逐, ；, 狻, 花,\n",
      "Nearest to 长: 永, 平, 远, 常, 逢, 滚, 当, 遥,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 曲, 舞, 柑, 吟,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 楚, 熄, 头,\n",
      "Nearest to 酒: 屠, 酿, 釂, 醪, 醺, 醑, 盏, 饤,\n",
      "Nearest to 此: 兹, 今, 每, 我, 幸, 何, 良, 为,\n",
      "Average loss at step  452000 :  3.8744664496183394\n",
      "Average loss at step  454000 :  3.8136585940122605\n",
      "Average loss at step  456000 :  3.822754657626152\n",
      "Average loss at step  458000 :  3.859060180783272\n",
      "Average loss at step  460000 :  3.8579347109794617\n",
      "Nearest to 子: 牧, 鸪, 跌, 潞, 姆, 缵, 乙, 荛,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 奈, 渐, 洒, 取,\n",
      "Nearest to 醉: 笑, 饮, 须, 睡, 醒, 拚, 醺, 猛,\n",
      "Nearest to 云: 雾, 烟, 缔, 霞, 缥, 霭, 瑛, 蓑,\n",
      "Nearest to 何: 邂, 岂, 多, 那, 无, 甚, 焉, 否,\n",
      "Nearest to 未: 不, 已, 难, 才, 易, 乍, 也, 初,\n",
      "Nearest to 看: 觑, 趁, 见, 听, 想, 与, 观, 任,\n",
      "Nearest to 天: 快, 逍, 蠲, 罨, 空, 绚, 茉, 岿,\n",
      "Nearest to 如: 似, 藁, 鳏, 佣, 胥, 依, 漠, 娜,\n",
      "Nearest to 水: 江, 山, 蹲, 碧, 颅, 纣, 麦, 稼,\n",
      "Nearest to 、: ，, 。, 怕, 恐, 得, ；, 到, （,\n",
      "Nearest to 长: 永, 殇, 平, 当, 滚, 跎, 逢, 常,\n",
      "Nearest to 歌: 唱, 乐, 遏, 箫, 曲, 舞, 硬, 阕,\n",
      "Nearest to 南: 北, 西, 东, 鄞, 熄, 阡, 头, 柂,\n",
      "Nearest to 酒: 屠, 釂, 斟, 酿, 醑, 盏, 醪, 杯,\n",
      "Nearest to 此: 兹, 今, 每, 何, 朅, 一, 幸, 敧,\n",
      "Average loss at step  462000 :  3.8779069638252257\n",
      "Average loss at step  464000 :  3.9156212604045866\n",
      "Average loss at step  466000 :  3.8866660737991334\n",
      "Average loss at step  468000 :  3.9303668891191483\n",
      "Average loss at step  470000 :  3.907803573966026\n",
      "Nearest to 子: 牧, 跌, 姆, 乙, 乐, 潞, 鸪, 戌,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 总, 奈, 荏, 却,\n",
      "Nearest to 醉: 笑, 醒, 睡, 醺, 酣, 困, 饮, 皴,\n",
      "Nearest to 云: 颅, 烟, 霭, 霞, 雾, 山, 雨, 天,\n",
      "Nearest to 何: 邂, 否, 焉, 还, 多, 昨, 岂, 那,\n",
      "Nearest to 未: 不, 乍, 已, 初, 易, 也, 顿, 才,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 见, 想, 任, 把,\n",
      "Nearest to 天: 逍, 快, 罨, 云, 蠲, 山, 淝, 帝,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 鳏, 溅, 旱, 而,\n",
      "Nearest to 水: 江, 山, 波, 颅, 派, 蹲, 秽, 澹,\n",
      "Nearest to 、: ，, 。, （, 怕, ；, 说, 入, 寞,\n",
      "Nearest to 长: 平, 永, 当, 殇, 滚, 遥, 逢, 亶,\n",
      "Nearest to 歌: 唱, 舞, 遏, 箫, 曲, 乐, 阕, 咽,\n",
      "Nearest to 南: 北, 西, 东, 阡, 鄞, 头, 熄, 横,\n",
      "Nearest to 酒: 屠, 盏, 釂, 斟, 杯, 酿, 醺, 篘,\n",
      "Nearest to 此: 兹, 今, 每, 良, 也, 予, 朅, 何,\n",
      "Average loss at step  472000 :  3.8816080883741377\n",
      "Average loss at step  474000 :  3.9001972724199296\n",
      "Average loss at step  476000 :  3.882958071947098\n",
      "Average loss at step  478000 :  3.8184242902994154\n",
      "Average loss at step  480000 :  3.843888380885124\n",
      "Nearest to 子: 跌, 牧, 潞, 荛, 籍, 鸪, 乐, 姆,\n",
      "Nearest to 尽: 了, 遍, 竟, 渐, 都, 断, 奈, 总,\n",
      "Nearest to 醉: 笑, 醒, 醺, 睡, 酣, 饮, 须, 困,\n",
      "Nearest to 云: 烟, 雾, 霞, 颅, 天, 缥, 杳, 旆,\n",
      "Nearest to 何: 邂, 昨, 岂, 不, 无, 多, 否, 焉,\n",
      "Nearest to 未: 不, 欲, 乍, 难, 易, 已, 先, 裛,\n",
      "Nearest to 看: 觑, 趁, 听, 见, 摘, 想, 对, 拂,\n",
      "Nearest to 天: 云, 罨, 空, 逍, 淝, 月, 霄, 山,\n",
      "Nearest to 如: 似, 奈, 鳏, 藁, 成, 佣, 漠, 蜒,\n",
      "Nearest to 水: 山, 江, 派, 波, 睥, 蹲, 涎, 碧,\n",
      "Nearest to 、: ，, 。, （, 酤, 逐, 怕, 狻, 逶,\n",
      "Nearest to 长: 永, 远, 逢, 遥, 常, 滚, 平, 当,\n",
      "Nearest to 歌: 唱, 乐, 箫, 遏, 舞, 曲, 柑, 吟,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 楚, 高,\n",
      "Nearest to 酒: 屠, 酿, 醪, 醺, 釂, 醑, 篘, 盏,\n",
      "Nearest to 此: 兹, 今, 每, 良, 我, 怎, 何, 难,\n",
      "Average loss at step  482000 :  3.8763033155202864\n",
      "Average loss at step  484000 :  3.822792060494423\n",
      "Average loss at step  486000 :  3.810667680442333\n",
      "Average loss at step  488000 :  3.8520083830356597\n",
      "Average loss at step  490000 :  3.8680932967662813\n",
      "Nearest to 子: 牧, 跌, 潞, 鸪, 荛, 缵, 姆, 乐,\n",
      "Nearest to 尽: 了, 遍, 竟, 都, 奈, 取, 洒, 渐,\n",
      "Nearest to 醉: 笑, 睡, 须, 醺, 饮, 醒, 拚, 尊,\n",
      "Nearest to 云: 雾, 烟, 缥, 霞, 缔, 瑛, 颅, 鸾,\n",
      "Nearest to 何: 邂, 多, 否, 岂, 焉, 那, 此, 尫,\n",
      "Nearest to 未: 不, 已, 才, 难, 易, 乍, 也, 初,\n",
      "Nearest to 看: 觑, 趁, 见, 摘, 想, 把, 听, 对,\n",
      "Nearest to 天: 快, 蠲, 逍, 罨, 空, 绚, 云, 濠,\n",
      "Nearest to 如: 似, 藁, 奈, 漠, 鳏, 胥, 依, 佣,\n",
      "Nearest to 水: 江, 山, 颅, 稼, 碧, 纣, 睥, 麦,\n",
      "Nearest to 、: ，, 。, 得, 怕, 恐, 说, ；, 是,\n",
      "Nearest to 长: 永, 殇, 平, 当, 逢, 跎, 遥, 滚,\n",
      "Nearest to 歌: 唱, 遏, 乐, 箫, 曲, 阕, 舞, 硬,\n",
      "Nearest to 南: 北, 西, 东, 阡, 熄, 头, 鄞, 柂,\n",
      "Nearest to 酒: 屠, 酿, 斟, 釂, 醑, 醪, 盏, 篘,\n",
      "Nearest to 此: 兹, 今, 每, 朅, 一, 何, 幸, 良,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  492000 :  3.895409930229187\n",
      "Average loss at step  494000 :  3.905799357533455\n",
      "Average loss at step  496000 :  3.8882675652503966\n",
      "Average loss at step  498000 :  3.9233505914211273\n",
      "Average loss at step  500000 :  3.8948059656620027\n",
      "Nearest to 子: 牧, 跌, 姆, 乐, 潞, 荛, 乙, 戌,\n",
      "Nearest to 尽: 了, 遍, 都, 竟, 奈, 却, 总, 荏,\n",
      "Nearest to 醉: 笑, 睡, 醒, 醺, 酣, 掏, 皴, 猛,\n",
      "Nearest to 云: 烟, 霞, 颅, 霭, 雾, 缥, 髭, 山,\n",
      "Nearest to 何: 多, 邂, 否, 焉, 昨, 还, 无, 岂,\n",
      "Nearest to 未: 不, 乍, 已, 易, 初, 顿, 也, 难,\n",
      "Nearest to 看: 觑, 趁, 听, 对, 见, 想, 任, 摘,\n",
      "Nearest to 天: 逍, 快, 罨, 蠲, 淝, 帝, 莹, 贬,\n",
      "Nearest to 如: 似, 奈, 藁, 漠, 溅, 鳏, 从, 躇,\n",
      "Nearest to 水: 江, 山, 颅, 波, 派, 溪, 澹, 稼,\n",
      "Nearest to 、: ，, 。, 怕, （, 尽, 窬, 恁, 入,\n",
      "Nearest to 长: 平, 永, 当, 殇, 逢, 劳, 遥, 滚,\n",
      "Nearest to 歌: 唱, 舞, 遏, 箫, 乐, 曲, 咽, 阕,\n",
      "Nearest to 南: 北, 西, 东, 阡, 头, 鄞, 熄, 涪,\n",
      "Nearest to 酒: 屠, 釂, 盏, 斟, 醺, 酿, 蘋, 杯,\n",
      "Nearest to 此: 兹, 今, 每, 良, 涪, 何, 幸, 予,\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(graph=graph) as session:\n",
    "  ckpt_path = tf.train.latest_checkpoint('./ckpt/')\n",
    "  # We must initialize all variables before we use them.\n",
    "  if ckpt_path:\n",
    "    saver.restore(session, ckpt_path)\n",
    "  else:\n",
    "    init.run()\n",
    "    print('Initialized')\n",
    "\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_inputs, batch_labels = generate_batch(\n",
    "        batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "    # We perform one update step by evaluating the optimizer op (including it\n",
    "    # in the list of returned values for session.run()\n",
    "    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += loss_val\n",
    "\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss /= 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step ', step, ': ', average_loss)\n",
    "      average_loss = 0\n",
    "\n",
    "    # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8  # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "        log_str = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log_str = '%s %s,' % (log_str, close_word)\n",
    "        print(log_str)\n",
    "  final_embeddings = normalized_embeddings.eval()\n",
    "\n",
    "  saver.save(session, os.path.join(os.getcwd(), 'ckpt/model'), global_step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('embedding.npy', final_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "  print('saving: '+filename)\n",
    "  plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving: /home/fei/AI/csdn课程/第11周/作业/quiz-w10-code/tsne.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "  from pylab import mpl\n",
    "  mpl.rcParams['font.sans-serif'] = ['Vera']\n",
    "  mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(os.getcwd(), 'tsne.png'))\n",
    "\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/fei/.local/lib/python3.6/site-packages/matplotlib/mpl-data/matplotlibrc'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
